Real-time refinement of subthalamic nucleus targeting using Bayesian decision-making on the root mean square measure

Area of Science:

• Neurosurgery
• Neurophysiology
• Medical Engineering

Background:

• The subthalamic nucleus (STN) is a critical target for deep brain stimulation (DBS) in advanced Parkinson's disease.
• Microelectrode recording (MER) is essential for precise STN targeting during DBS surgery.
• Improving real-time identification of STN entry and exit points can enhance surgical outcomes.

Purpose of the Study:

• To develop and validate a real-time computational procedure for accurately identifying STN borders during MER.
• To assess the efficacy of using normalized root mean square (NRMS) and estimated anatomical distance to target (EDT) for STN localization.
• To provide a basis for a fully automated intraoperative targeting system.

Main Methods:

• Utilized normalized root mean square (NRMS) of short (5-second) MER signals and estimated anatomical distance to target (EDT).
• Intraoperative expert neurophysiologist defined electrode tip location relative to the STN (before, within, after).
• Employed Bayesian probability calculations and bootstrapping validation on data from 46 trajectories in 27 patients.

Main Results:

• Achieved a mean +/- SD error of 0.18 +/- 0.84 mm for STN entry point prediction.
• Achieved a mean +/- SD error of 0.50 +/- 0.59 mm for STN exit point prediction.
• Resulted in a deviation of 0.30 +/- 0.28 mm from the expert's target center, demonstrating high accuracy.

Conclusions:

• The NRMS-EDT Bayesian predictor offers a simple, computationally efficient, and spike-sorting-independent method for intraoperative STN localization.
• This approach is robust to variations in electrode parameters.
• The findings support the development of automated systems to refine STN border identification in DBS surgery.